Polyolefin resins, including polyethylene, may be manufactured in various reactor systems, including systems comprising a fluidized bed reactor. In such processes, the polymer product discharged from the reaction zone comprises solid polymer granules and volatiles including unreacted hydrocarbons from the monomer, comonomer, and catalyst. The volatiles may be dissolved in, bound to, or otherwise attached to the polymer granules and/or in the vapor space external to the polymer granules. Heavy olefin monomers often used as comonomers in polyethylene polymerization processes, such as 1-hexene, are especially soluble in low density polyethylene. The process of reducing the volatiles down to acceptable levels in the polymer product is referred to in the art as resin degassing or purging.
A polymer product may be purged by depressurizing the resin and stripping it with a light purge gas, such as nitrogen. In these processes, the polymer product is transferred to a lower pressure purge bin. The polymer product enters the upper portion of the vessel and is subjected to purge gas entering the vessel through ports or openings at the bottom of the vessel and possibly along the sides and other areas. It sweeps through the granular resin and exits the purge bin. The purged polymer product is discharged and conveyed to further downstream processes, while recovered hydrocarbons are swept out in the purge gas and may be recycled back to the reactor. Background references for polymer purge systems include U.S. Pat. Nos. 3,797,707; 4,286,883; 4,372,758; 4,731,438; 4,758,654; 5,292,863; 5,462,351; 8,470,082, U.S. Patent Application Publication No. 2011/0201765, and EP 2 172 494 A.
Effective and efficient purging is important for safety and environmental reasons. The volatiles must be removed or reduced to an appropriate level before the polymer product is exposed to the atmosphere. Additionally, it is economically advantageous to recover as much of the hydrocarbons as possible, to minimize the use of additional raw materials and compression and pumping energy. However, unpredicted or undetected events could occur in a polyolefin reactor system and affect purge performance. For example, during a reactor upset in a fluidized bed reactor, polymer sheets or chunks could be produced and transferred from the reactor to the purge bin. These sheets and chunks within the purge bin could lead to poor distribution of the purge gas, reducing purge performance. Without a good method or system for modeling purge performance, these events may go unrecognized and lead to substantial problems with downstream equipment and processing, as well as product quality and transport.
It is very challenging to create useful and accurate models of purge systems, to predict the variables that affect purging, and to determine when an undetected event in the reactor system is affecting purge performance. Conventionally, it has been thought based on well-known diffusion mechanisms that purge efficiency depended on the size of the polymer granules in the polymer product, and could thus be improved by reducing the diameter of the polymer granules. It is not always desirable or possible, however, to reduce the diameter of the polymer granules being produced. Furthermore, models incorporating a presumed relationship between purge efficiency and polymer granule size have shown to be inaccurate in modeling purge performance in some systems.
Other purge models have focused on finding a relationship between the concentration of volatiles at the inlet of the purge bin and at the exit of the purge bin. The output of these models has typically suggested that this relationship is only weakly correlated to the mass flow rate of purge gas in the purge bin. Thus, these models typically indicate that a very large purge bin is required to achieve a given purge efficiency, rather than providing useful information on other variables that may be more readily changed to impact purge efficiency.
There is a need for more useful and accurate methods for modeling purge system behavior, and for models that more accurately predict purge performance and the impact of changes to relevant variables. There is a need for models that can provide real-time information on events within the reactor system that are affecting purge performance and for improved purge systems useful with those models.